The proposed research is designed to fully analyze the prospectively accumulated data on 973 enrolled patients in the Multicenter Study of Silent Myocardial Ischemia (MSSMI). The primary goal is to determine the usefulness, or the lack thereof, of ambulatory electrocardiographic (AECG) monitoring for identifying coronary patients with jeopardized ischemic myocardium at risk for ischemic cardiac events. The aims of this grant are: 1) to evaluate the reproducibility of current methods for detecting ischemic-type changes (ST depression) on AECG; 2) to improve the accuracy and reliability of contemporary scanner-interactive methodology that we and others have been utilizing for identifying ischemic-type ST-segment changes on the AECG; 3) to complete the development on a new, innovative, and reproducible computer-based technique for automatic (operator-independent), quantitative identification of beat-to-beat ischemic-type ST depression on the MSSMI AECG tapes; 4) to evaluate the clinical utility of the improved and new ST-segment analytic techniques to identify coronary patients at risk for natural cardiac events (unstable angina, non-fatal myocardial infarction, and cardiac death) during 2-year follow-up in the overall MSSMI population, in selected subgroups (gender, age), and in prespecified high-risk ischemic subsets; and 5) to determine the optimal cost- effective strategy for sequencing the assessment of silent, jeopardized ischemic myocardium by clinical variables and non-invasive tests to identify coronary patients at risk for subsequent ischemic cardiac events. This research will utilize statistical techniques for evaluating reproducibility of detecting ST depression on AECG (kappa statistics, analyses of variance, Cochran's Q-test), for determining ischemic risk factors for time-to-cardiac events (Cox regression analyses), and for cost-effectiveness analyses (receiver-operator characteristic curves). Basic to an understanding of myocardial ischemia, silent or symptomatic, is knowledge of the effectiveness of the technologies used to detect it, the true prevalence of ischemia in a well defined, representative coronary population, and predictive value of a positive ischemic test in the population and in clinically relevant subsets. The information derived from the analytic phase of this research program should improve the diagnosis and management of patients with ischemic heart disease.